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There have been major advances in the field of data science in the last few decades, and these have been utilized for different engineering disciplines and applications. Artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms have been utilized for civil structural health Monitoring (SHM) especially for damage detection applications using sensor data. Although ML and DL methods show superior learning skills for complex data structures, they require plenty of data for training. However, in SHM, data collection from civil structures can be expensive and time taking; particularly getting useful data (damage associated data) can be challenging. The objective of this study is to address the data scarcity problem for damage detection applications. This paper employs 1-D Wasserstein Deep Convolutional Generative Adversarial Networks using Gradient Penalty (1-D WDCGAN-GP) for synthetic labelled acceleration data generation. Then, the generated data is augmented with varying ratios for the training data set of a 1-D deep convolutional neural network (1-D DCNN) for damage detection application. The damage detection results show that the 1-D WDCGAN-GP can be successfully utilized to tackle data scarcity in vibration-based damage detection applications of civil structures.

Infertility has emerged as a significant health issue impacting individuals’ lives. In prior investigations, image classification has been applied to identify morphologic abnormalities associated with infertility issues. However, the limited data availability has impeded high performance. In the field of image augmentation techniques, particularly concerning generative adversarial networks (GANs), an alternative approach can encounter a significant issue known as mode collapse. This phenomenon arises when the generator consistently produces a restricted set of identical or highly similar images, which may negatively affect the overall performance and accuracy of the model. Consequently, the aim of this study is to mitigate mode collapse by employing loss‐based ensemble GAN framework, formulated based on the integration of two distinct GAN models. In addition, a comprehensive analysis is carried out using an expanded approach involving three GAN models in conjunction with a spatial augmentation technique. The Shifted Window Transformer model achieves 95.37% accuracy on the HuSHeM dataset, outperforming other classification models. This finding shows enhanced accuracy relative to earlier studies using the identical dataset. A loss‐based ensemble generative adversarial network (GAN) framework is proposed to address mode collapse in sperm morphology classification. By integrating spatial augmentation and multiple GAN models, the study enhances synthetic data quality. The Shifted Window Transformer achieves 95.37% accuracy on the HuSHeM dataset, outperforming previous approaches and demonstrating the effectiveness of the proposed augmentation strategy.

In this paper, we propose a hierarchical multi-modal multi-label attribute classification model for anime illustrations using a graph convolutional network (GCN). Our focus is on the challenging task of multi-label attribute classification, which requires capturing subtle features intentionally highlighted by creators of anime illustrations. To address the hierarchical nature of these attributes, we leverage hierarchical clustering and hierarchical label assignments to organize the attribute information into a hierarchical feature. The proposed GCN-based model effectively utilizes this hierarchical feature to achieve high accuracy in multi-label attribute classification. The contributions of the proposed method are as follows. Firstly, we introduce GCN to the multi-label attribute classification task of anime illustrations, enabling the capturing of more comprehensive relationships between attributes from their co-occurrence. Secondly, we capture subordinate relationships among the attributes by adopting hierarchical clustering and hierarchical label assignment. Lastly, we construct a hierarchical structure of attributes that appear more frequently in anime illustrations based on certain rules derived from previous studies, which helps to reflect the relationships between different attributes. The experimental results on multiple datasets show that the proposed method is effective and extensible by comparing it with some existing methods, including the state-of-the-art method.

The most common causes of spine fractures, or vertebral column fractures (VCF), are traumas like falls, injuries from sports, or accidents. CT scans are affordable and effective at detecting VCF types in an accurate manner. VCF type identification in cervical, thoracic, and lumbar (C3-L5) regions is limited and sensitive to inter-observer variability. To solve this problem, this work introduces an autonomous approach for identifying VCF type by developing a novel ensemble model of Vision Transformers (ViT) and best-performing deep learning (DL) models. It assists orthopaedicians in easy and early identification of VCF types. The performance of numerous fine-tuned DL architectures, including VGG16, ResNet50, and DenseNet121, was investigated, and an ensemble classification model was developed to identify the best-performing combination of DL models. A ViT model is also trained to identify VCF. Later, the best-performing DL models and ViT were fused by weighted average technique for type identification. To overcome data limitations, an extended Deep Convolutional Generative Adversarial Network (DCGAN) and Progressive Growing Generative Adversarial Network (PGGAN) were developed. The VGG16-ResNet50-ViT ensemble model outperformed all ensemble models and got an accuracy of 89.98%. Extended DCGAN and PGGAN augmentation increased the accuracy of type identification to 90.28% and 93.68%, respectively. This demonstrates efficacy of PGGANs in augmenting VCF images. The study emphasizes the distinctive contributions of the ResNet50, VGG16, and ViT models in feature extraction, generalization, and global shape-based pattern capturing in VCF type identification. CT scans collected from a tertiary care hospital are used to validate these models.

Conventional radar systems are often unable to produce highly accurate results for target classification and identification via linear frequency modulation (LFM) signals. The potential of artificial intelligence, particularly deep learning, has been applied in various fields, which promotes utilizing them in the context of target classification in radar systems. However, to train deep learning models for this task, large datasets of LFM radar signals are required, which are practically difficult to obtain due to the time, effort, and involved high cost. Therefore, the presented work spots the light on utilizing the recent one-dimensional generative adversarial network (GAN) and Wasserstein GAN (WGAN) models to synthesize a large time-series LFM signal dataset from a reference smaller one. Moreover, the work fairly judges the generated LFM signals realistic via a decent qualitative and quantitative analysis, unlike other studies which rely solely on qualitative evaluation by human observers. The proposed study outcome reveals the WGAN’s efficiency in synthesizing high-quality LFM signals while reducing the training time and resource requirements.

Deep convolutional generative adversarial networks (DCGANs) are newly developed tools for generating synthesized images. To determine the clinical utility of synthesized images, we generated breast ultrasound images and assessed their quality and clinical value. After retrospectively collecting 528 images of 144 benign masses and 529 images of 216 malignant masses in the breasts, synthesized images were generated using a DCGAN with 50, 100, 200, 500, and 1000 epochs. The synthesized (n = 20) and original (n = 40) images were evaluated by two radiologists, who scored them for overall quality, definition of anatomic structures, and visualization of the masses on a five-point scale. They also scored the possibility of images being original. Although there was no significant difference between the images synthesized with 1000 and 500 epochs, the latter were evaluated as being of higher quality than all other images. Moreover, 2.5%, 0%, 12.5%, 37.5%, and 22.5% of the images synthesized with 50, 100, 200, 500, and 1000 epochs, respectively, and 14% of the original images were indistinguishable from one another. Interobserver agreement was very good (|r| = 0.708–0.825, p < 0.001). Therefore, DCGAN can generate high-quality and realistic synthesized breast ultrasound images that are indistinguishable from the original images.

Image generation is a hot topic in the field of machine learning and computer vision. As a representative of its algorithm, the Generative Adversarial Network (GAN) has the problem of mode collapse in practice. The proposed Dual Discriminator Weighted Mixture Generative Adversarial Network (D2WMGAN) approach can cope with this problem. On the one hand, the D2WMGAN uses the mixed distribution of multiple generators to approximate the real distribution, in order to prevent the extreme situation that multiple generators learn the same distribution and generate the same class of samples, with a classifier to play games with generators to make different generators learn different distributions. On the other hand, the objective function of D2WMGAN weights the Kullback–Leibler (KL) divergence and the reverse KL divergence, and uses their complementary characteristics to improve the quality and diversity of samples from the generators. Then, the theoretical conditional optimality of the D2WMGAN is proved theoretically, which shows that multiple generators can learn the real data distribution in the case of the optimal discriminator and classifier. Finally, extensive experiments are conducted on a large amount of synthetic data and real-world large-scale datasets (such as, CIFAR-10 and MNIST), and the commonly used GAN evaluation indicators (Wasserstein distance, JS divergence, Inception score, and Frechet Inception Distance) are introduced for comparative analysis. Experimental results show that the proposed D2WMGAN approach can better learn multiple mode data, generate rich realistic samples, and effectively solve the problem of mode collapse.

In a complex industrial environment, it is difficult to obtain hot rolled strip steel surface defect images. Moreover, there is a lack of effective identification methods. In response to this, this paper implements accurate classification of strip steel surface defects based on generative adversarial network and attention mechanism. Firstly, a novel WGAN model is proposed to generate new surface defect images from random noises. By expanding the number of samples from 1360 to 3773, the generated images can be further used for training classification algorithm. Secondly, a Multi-SE-ResNet34 model integrating attention mechanism is proposed to identify defects. The accuracy rate on the test set is 99.20%, which is 6.71%, 4.56%, 1.88%, 0.54% and 1.34% higher than AlexNet, VGG16, ShuffleNet v2 1×, ResNet34, and ResNet50, respectively. Finally, a visual comparison of the features extracted by different models using Grad-CAM reveals that the proposed model is more calibrated for feature extraction. Therefore, it can be concluded that the proposed methods provide a significant reference for data augmentation and classification of strip steel surface defects.

Recently, artificial intelligence (AI) based on IoT sensors has been widely used, which has increased the risk of attacks targeting AI. Adversarial examples are among the most serious types of attacks in which the attacker designs inputs that can cause the machine learning system to generate incorrect outputs. Considering the architecture using multiple sensor devices, hacking even a few sensors can create a significant risk; an attacker can attack the machine learning model through the hacked sensors. Some studies demonstrated the possibility of adversarial examples on the deep neural network (DNN) model based on IoT sensors, but it was assumed that an attacker must access all features. The impact of hacking only a few sensors has not been discussed thus far. Therefore, in this study, we discuss the possibility of attacks on DNN models by hacking only a small number of sensors. In this scenario, the attacker first hacks few sensors in the system, obtains the values of the hacked sensors, and changes them to manipulate the system, but the attacker cannot obtain and change the values of the other sensors. We perform experiments using the human activity recognition model with three sensor devices attached to the chest, wrist, and ankle of a user, and demonstrate that attacks are possible by hacking a small number of sensors.

With the widespread adoption of Generative Adversarial Networks (GANs) in image generation and processing, enhancing their generation quality and visualization capabilities has become a prominent research focus. This study introduces a deep learning framework that integrates multi-scale information chart generation with visualization enhancement to improve the performance of GAN-based image generation models across various domains. Based on the Self-Attention Generative Adversarial Network (SAGAN), which leverages self-attention mechanisms to capture long-range dependencies in images, the proposed approach significantly enhances image quality and detail representation. The framework incorporates a multi-scale feature extraction method to optimize the feature maps at each layer of the generative network. Experimental results demonstrate that SAGAN outperforms traditional GAN models in terms of image clarity, detail preservation, and visual effects. The proposed model achieves notable improvements in diversity and generalization, with a mutual information content of 0.91, clustering uniformity of 0.89, and inter-cluster dissimilarity of 0.92 on the CelebA dataset. Furthermore, in terms of image quality, SAGAN attains a Structural Similarity Index Measure (SSIM) of 0.94 and a Peak Signal-to-Noise Ratio (PSNR) of 30.1, surpassing traditional GANs by a significant margin.Article HighlightsInnovatively combining the self-attention mechanism with multi-scale feature extraction, it significantly improves the quality and detail performance of image generation, and brings a breakthrough in the field of data visualization.On the CelebA dataset, the mutual information of the model reaches 0.91, clustering uniformity 0.89, and inter-cluster variability 0.92; in terms of image quality, the SSIM value is 0.94 and the PSNR value is 30.1, which significantly exceeds the traditional GAN model.Through strategies such as color adjustment, style optimization and line detail enhancement, the visual effect of the generated charts is further optimized, so that the charts not only accurately convey the data, but also have stronger readability and aesthetics.